The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project's maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare. Originally developed at the National University of Singapore, SINGA joined ASF's incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.

So if you're looking to learn one of the most desirable skills in tech, you've come to the right place. We've already gone over the top machine learning libraries and open source projects, so now we're taking a close look at frameworks. Developed by a team at the National University of Singapore, Apache Singa is a flexible and scalable deep learning platform for big data analytics. This deep learning framework provides a flexible architecture for scalable distributed training on large volumes of data. Singa is extensible to run over a wide range of hardware.

When delving into the world of machine learning (ML), choosing one framework from many alternatives can be an intimidating task. You might already be familiar with the names, but it's useful to evaluate the options during the decision-making process. There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you're a beginner. Being accustomed to the popular ML frameworks is necessary when it comes to choosing one to build your application. This is why we compiled a list of the top 10 machine learning frameworks.